Video super-resolution (VSR) is reconstructing high-resolution videos from low resolution
ones. Recently, the VSR methods based on deep neural networks have made great …

Super-resolution (SR) technique reconstructs a higher-resolution image or sequence from
the observed LR images. As SR has been developed for more than three decades, both …

Video super-resolution (VSR) approaches tend to have more components than the image
counterparts as they need to exploit the additional temporal dimension. Complex designs …

Video restoration tasks, including super-resolution, deblurring, etc, are drawing increasing
attention in the computer vision community. A challenging benchmark named REDS is …

Recent advances in video super-resolution have shown that convolutional neural networks
combined with motion compensation are able to merge information from multiple low …

Recently, several models based on deep neural networks have achieved great success in
terms of both reconstruction accuracy and computational performance for single image …

Convolutional neural networks have enabled accurate image super-resolution in real-time.
However, recent attempts to benefit from temporal correlations in video super-resolution …

Previous CNN-based video super-resolution approaches need to align multiple frames to
the reference. In this paper, we show that proper frame alignment and motion compensation …

Convolutional neural networks (CNN) are a special type of deep neural networks (DNN).
They have so far been successfully applied to image super-resolution (SR) as well as other …

Stereo image pairs can be used to improve the performance of super-resolution (SR) since
additional information is provided from a second viewpoint. However, it is challenging to …